arXiv subjects

Keith R Abrams

Publications and source records attributed to Keith R Abrams.

9 recordsLinked to original sources

arXiv2026

Meta-analysis and network meta-analysis of time-to-event outcomes with non-proportional hazards: a Bayesian time-varying hazard ratio approach

Background: Often when undertaking meta-analyses of time-to-event (TTE) outcomes, especially in a Health Technology Assessment context, a hazard ratio (HR) scale is used. However, issues arise when there is evidence of non-proportional hazards in some of the studies included. A number of methods have been advocated, but their use has been limited by either their complexity and/or the ease with which their results can be used in HTA. An alternative approach is to assume a treatment-log(time) interaction within a Cox proportional hazards model for each study, and to then undertake a bivariate meta-analysis of the resulting treatment and interaction coefficients, so that an overall time-varying HR (TVHR) can be obtained. Methods: A TVHR approach was applied to a meta-analysis of chemotherapy compared to Standard of Care for advanced recurrent gastric cancer, and in which Progression-Free Survival (PFS) was an outcome. The approach was also applied to a network meta-analysis (NMA) evaluating overall survival (OS) in advanced BRAF-mutated melanoma. Results: Five trials in the advanced gastric cancer meta-analysis displayed evidence of non-proportional hazards for PFS. Using a TVHR model produced HRs ranging from 0.83 (CrI:0.75-0.91) at 0.5 years to 0.99 (CrI:0.79-1.23) at 3.5 years. Three studies showed evidence of non-proportional hazards in the advanced BRAF-mutated melanoma NMA for OS. Using a TVHR model, nivolumab plus ipilimumab demonstrated consistent superiority from month 7 onwards, with a HR improving from 0.37 (CrI:0.26-0.51) at one year to 0.24 (CrI:0.12-0.45) at five years. Conclusions: A TVHR approach to the meta-analysis or NMA of TTE outcomes when the proportional hazards assumption appears not to hold, produces an intuitive solution which can be readily used in HTA.

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arXiv2026

Bayesian Profile Regression using Variational Inference to Identify Clusters of Multiple Long-Term Conditions Conditioning on Mortality in Population-Scale Data

Multiple long-term conditions (MLTC) are increasingly observed in clinical practice globally. Clustering methods to group diseases into commonly co-occurring clusters have been of interest for further understanding of how MLTC group together and their associated impact on patient outcomes. However, such approaches require large, often population-scale datasets. Bayesian Profile Regression (BPR) is a statistical model that combines a Dirichlet Process Mixture model with a hierarchical regression model, in order to form clusters of items conditional on covariates and an outcome of interest. We developed a BPR model using full-rank Stochastic Variational Inference (SVI) for application in large-scale data. We assessed it's performance using simulation studies comparing fits using the No-U-turn (NUTS) sampler and full-rank SVI. We then fit a BPR model to find clusters of MLTC in a population-scale data held in the Secure Anonymised Information Linkage (SAIL) databank. We found results from full-rank SVI compared well with results from NUTS in a simulation study, and the improved fitting performance allowed for fitting models in population-scale datasets. There were 1,296,463 individuals in our electronic health record (EHR) cohort. The clustering model was conditioned on age at cohort entry, socioeconomic deprivation and sex with mortality as the outcome. We used the Elixhauser comorbidity index disease definitions, and found there were 33 disease clusters. We found that clusters featuring metastatic cancer and cardiovascular diseases, such as congestive heart failure, were most strongly associated with the probability of mortality. Our findings show that SVI can be a useful and accurate method for fitting Bayesian models, especially when the dataset size would make Monte Carlo methods prohibitively time consuming or impossible.

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arXiv2025

Bayesian joint modelling of longitudinal biomarkers to enable extrapolation of overall survival: an application using larotrectinib trial clinical data

Objectives To investigate the use of a Bayesian joint modelling approach to predict overall survival (OS) from immature clinical trial data using an intermediate biomarker. To compare the results with a typical parametric approach of extrapolation and observed survival from a later datacut. Methods Data were pooled from three phase I/II open-label trials evaluating larotrectinib in 196 patients with neurotrophic tyrosine receptor kinase fusion-positive (NTRK+) solid tumours followed up until July 2021. Bayesian joint modelling was used to obtain patient-specific predictions of OS using individual-level sum of diameter of target lesions (SLD) profiles up to the time at which the patient died or was censored. Overall and tumour site-specific estimates were produced, assuming a common, exchangeable, or independent association structure across tumour sites. Results The overall risk of mortality was 9% higher per 10mm increase in SLD (HR 1.09, 95% CrI 1.05 to 1.14) for all tumour sites combined. Tumour-specific point estimates of restricted mean , median and landmark survival were more similar across models for larger tumour groups, compared to smaller tumour groups. In general, parameters were estimated with more certainty compared to a standard Weibull model and were aligned with the more recent datacut. Conclusions Joint modelling using intermediate outcomes such as tumour burden can offer an alternative approach to traditional survival modelling and may improve survival predictions from limited follow-up data. This approach allows complex hierarchical data structures, such as patients nested within tumour types, and can also incorporate multiple longitudinal biomarkers in a multivariate modelling framework.

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arXiv2025

Evaluating amyloid-beta as a surrogate endpoint in trials of anti-amyloid drugs in Alzheimer's disease: a Bayesian meta-analysis

The use of amyloid-beta (A$β$) clearance to support regulatory approvals of drugs in Alzheimer's disease (AD) remains controversial. We evaluate A$β$ as a potential trial-level surrogate endpoint for clinical function in AD using a meta-analysis. Randomised controlled trials (RCTs) reporting data on the effectiveness of anti- A$β$ monoclonal antibodies (MABs) on A$β$ and clinical outcomes were identified through a literature review. A Bayesian bivariate meta-analysis was used to evaluate surrogate relationships between the treatment effects on A$β$ and clinical function, with the intercept, slope and variance quantifying the trial level association. The analysis was performed using RCT data both collectively across all MABs and separately for each MAB through subgroup analysis. The latter analysis was extended by applying Bayesian hierarchical models to borrow information across treatments. We identified 23 RCTs with 39 treatment contrasts for seven MABs. The association between treatment effects on A$β$ and Clinical Dementia Rating - Sum of Boxes (CDR-SOB) across all MABs was strong: with intercept of -0.03 (95% credible intervals: -0.16, 0.11), slope of 1.41 (0.60, 2.21) and variance of 0.02 (0.00, 0.05). For individual treatments, the surrogate relationships were suboptimal, displaying large uncertainty. The use of hierarchical models considerably reduced the uncertainty around key parameters, narrowing the intervals for the slopes by an average of 71% (range: 51%-95%) and for the variances by 28% (7%-65%). Our results suggest that A$β$ is a potential surrogate endpoint for CDR-SOB when assuming a common surrogate relationship across all MABs. When allowing for information-sharing, the surrogate relationships improved, but only for lecanemab and aducanumab was the improvement sufficient to support a surrogate relationship.

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arXiv2023

Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis

Objective: We aimed to develop a meta-analytic model for evaluation of predictive biomarkers and targeted therapies, utilising data from digital sources when individual participant data (IPD) from randomised controlled trials (RCTs) are unavailable. Methods: A Bayesian network meta-regression model, combining aggregate data (AD) from RCTs and IPD, was developed for modelling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using target trial emulation approach, or digitised Kaplan-Meier curves. The model is illustrated using two examples; breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker. Results: The model developed allowed for estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxane did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor (EGFR) inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the sub-group specific treatment effect estimates by up to 49%. Conclusion: Utilisation of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone.

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arXiv2022

Use of copula to model within-study association in bivariate meta-analysis of binomial data at the aggregate level a Bayesian approach and application to surrogate endpoint evaluation

Bivariate meta-analysis provides a useful framework for combining information across related studies and has been utilised to combine evidence from clinical studies to evaluate treatment efficacy on two outcomes. It has also been used to investigate surrogacy patterns between treatment effects on the surrogate endpoint and the final outcome. Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final outcome and to predict clinical benefit or harm. The standard bivariate meta-analytic approach models the observed treatment effects on the surrogate and the final outcome outcomes jointly, at both the within-study and between-studies levels, using a bivariate normal distribution. For binomial data, a normal approximation on log odds ratio scale can be used. However, this method may lead to biased results when the proportions of events are close to one or zero, affecting the validation of surrogate endpoints. In this paper, we explore modelling the two outcomes on the original binomial scale. Firstly, we present a method that uses independent binomial likelihoods to model the within-study variability avoiding to approximate the observed treatment effects. However, the method ignores the within-study association. To overcome this issue, we propose a method using a bivariate copula with binomial marginals, which allows the model to account for the within-study association. We applied the methods to an illustrative example in chronic myeloid leukemia to investigate the surrogate relationship between complete cytogenetic response (CCyR) and event-free-survival (EFS).

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arXiv2021

Methods for the inclusion of real world evidence in network meta-analysis

Background: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. We investigate methods for the inclusion of RWE in NMA and its impact on the uncertainty around the effectiveness estimates. Methods: A range of methods for inclusion of RWE in evidence synthesis, including Bayesian hierarchical and power prior models, were investigated by applying them to an example in relapsing remitting multiple sclerosis. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. Power prior NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs; however, this also increased the level of uncertainty. Conclusion: The power prior approach for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

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arXiv2018

Bivariate network meta-analysis for surrogate endpoint evaluation

Surrogate endpoints are very important in regulatory decision-making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on the pairwise methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods which combine data on treatment effects on the surrogate and final outcomes, from trials investigating heterogeneous treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the surrogacy patterns across multiple trials (different populations) within a treatment contrast and across treatment contrasts, thus enabling predictions of the treatment effect on the final outcome for a new study in a new population or investigating a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using simulated data and an illustrative example in advanced colorectal cancer. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatments for which surrogacy holds, thus leading to better predictions.

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arXiv2017

Bayesian multi-parameter evidence synthesis to inform decision-making: a case study in hormone-refractory metastatic prostate cancer

In health technology assessment, decisions are based on complex cost-effectiveness models which, to be implemented, require numerous input parameters. When some of relevant estimates are not available the model may have to be simplified. Multi-parameter evidence synthesis allows to combine data from diverse sources of evidence resulting in obtaining estimates required in clinical decision-making that otherwise may not be available. We demonstrate how bivariate meta-analysis (BVMA) can be used to predict unreported estimate of a treatment effect enabling implementation of multi-state Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer (mHRPC). BVMA was used to model jointly available data on treatment effects on overall survival (OS) and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel. Predicted treatment effect on PFS allowed implementation of a three-state Markov model comprising of stable disease, progressive disease and death states, whilst lack of the estimate restricted the model to two-state model (stable disease and death states). The two-state and three-state models were compared by calculating incremental cost-effectiveness ratios, which was much lower in the three-state model: £21966 per QALY gained compared to £30026 obtained from the two-state model. In contrast to the two-state model, the three-state model has the advantage of distinguishing patients who progressed from those who did not progress. The use of advanced meta-analytic technique helped to obtain relevant parameter estimate to populate a model which describes natural history more accurately, and at the same helped to prevent valuable clinical data from being discarded.

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